footwear pref survey sec d gr 2

8/12/2019 Footwear Pref Survey Sec D Gr 2

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SEC – D Group Numb

Swayambhu Dutta U

Swastik Mohapatra U

Stalin Mohapatra U

Digant Bhatt

Context : A well Known brand – X isplanning to launch a new brand and beforethat it wants to check that where it standson the basis of the Customer Loyalty.

It wants to check the demographics andthe Loyalty and thereby find a

Discriminant equation model .



Male79%

Female21%

Gender Distribution

Male Female

Married26%

Marita

Sing

0

3842

5 20

10

20

30

40

50

Below 16 16-25 26-35 36-45 46 & Above

Age DemographicDistribution

Age Demographic Distribution

17

34

05

101520253035

40

Below 3 L 3L-5L

Annual Inc

Annual

714

35 29

205

10152025303540

Expenditure on Footwears

Expenditure on Footwears

DemographyDiscriminant

AnalysisModel

Accuracy ofthe Model

StatisticalSignificance

BestDiscriminator

DiscriminCriterio



Discriminant Analysis Model : Categorizing the Customers into Loyal or non-Loyal

= + ∗ + ∗ +

StandardiDiscrim

Co

Income

Expenditure

Freq

Variables Meaning

Income Average income

Expenditure Average Purchase perTransaction

Freq Frequency of Purchase in a year

= + ∗ . + ∗ . + ∗


AnalysisModel



BestDiscriminator

DiscriminCriterio



Classification Results a

LoyaltyPredicted Group Membership

1 2

OriginalCount 1 352 1% 1 94.6

2 2.0 9a. 96.6% of original grouped cases correctly classified.

Here Normal Customers (Code = 1) and Loyal Customers (Code = 2)

Customer Types ValuesOriginal Normal Customer(Code = 1) 35+2 = 37

Original Loyal Customer(Code = 2) 1+49= 50

Total Customers 37+50 = 87

Predicted Normal Customers(Code = 1) 35+1= 36

Predicted Loyal Customers(Code = 2) 2+49= 51

Accuracy Ratio =(94.6 % + 98 % )/ 2

= 96.3%

Finding the AccDiscriminant Analy


AnalysisModel



BestDiscriminator

DiscriminCriterio



Eigenvalues

FunctionEigenvalue % of Variance

Cumulative%

CanonicalCorrelation

1 1.562a 100.0 100.0 .781

a. First 1 canonical discriminant functions were used in theanalysis.

Wilks' Lambda

Test ofFunction(s)

Wilks'Lambda Chi-square df Sig.

1 .390 78.540 3 .000

The Discriminant Msignificant Eigen Vgreater than 1

For our Model : Eig1.562

The Discriminant Msignificant Wilks’sbe lesser than 0.5

For our Model :0.390


AnalysisModel



BestDiscriminator

DiscriminCriterio



′ = + ∗ + ∗ + ∗

= − . + ∗ .

∗ . ∗ .

The Above equation does not gives us the eqhelps us to understand that which one of theis important

Here we can see that that the frequenc

is the most important factor


AnalysisModel



BestDiscriminator

DiscriminCriterio



Functions at Group Centroids - These are the means ofthe discriminant function scores by group for each functioncalculated. If we calculated the scores of the first function

for each case in our dataset, and then looked at themeans of the scores by group, we would find thatthe Normal Customer group has a mean of 1.436,the Loyal Customer group has a mean of -1.063. Weknow that the function scores have a mean of zero, andwe can check this by looking at the sum of the groupmeans multiplied by the number of cases in each group.

Misfit Data Data Points( Case Number)

Original Normal but will be Loyal 25, 51

Original Loyal but will be Norma 84


AnalysisModel



BestDiscriminator

DiscriminCriterio



= + ∗ . + ∗ . + ∗

We can Conclude that with the help of the below equation we can predict whether the customer isLoyal or Normal if we can get some extra variables like Income, Expenditure & FrequePurchase .


AnalysisModel



BestDiscriminator

DiscriminCriterio



Data Name Files

Data

Data in SPSS

Output File of SPSS

Word output File of SPSS

footwear pref survey sec d gr 2

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